Data and intelligence
Data and intelligence is the brain of the Gamble Hub, a system that senses, analyzes and acts. In classical models, data is the archive accessed after events. In Gamble Hub, they become a livestream, feeding solutions, models and automatic reactions.
Every event in the ecosystem - from click to transaction - turns into a signal. These signals are processed by machine models that recognize patterns, predict behavior, and help operators make decisions faster than manually possible.
The main idea: data is not collected for the sake of a report, it creates the semantic fabric of the system. Gamble Hub builds a chain:- telemetry → models → signals → operations.
1. Telemetry. The network captures millions of microevents: player activity, RTP changes, API delays, betting streams, user behavior.
2. Models. Machine learning algorithms identify anomalies, predict load peaks, determine stable patterns of profitability and risks.
3. Signals. Models generate signals - recommendations, warnings, automatic actions.
4. Operations. The system itself performs part of the decisions: adjusts the limits, informs operators, changes configurations and reports on opportunities.
This is how a self-learning infrastructure is created, where intelligence does not replace a person, but helps him see further and act faster.
The Gamble Hub data architecture is built around the principles of:- Transparency and verification. Each number has a fixation source and time.
- Contextuality. The model does not work with abstract values, but with reference to currencies, regions, providers and players.
- Continuing education. Algorithms are updated as new data becomes available, avoiding "outdated assumptions."
- Integration with operations. Models do not live in isolation - they are built into interfaces and APIs, turning analytics into action.
- Operational intelligence - instant reaction to events and deviations.
- Strategic intelligence - analysis of trends and formation of growth scenarios.
- Collective intelligence - synchronizing knowledge between circuits and participants.
Gamble Hub converts data from a byproduct into system energy.
Intelligence here is not a module or a service, but a built-in property of architecture that makes the ecosystem capable of introspection, adaptation and prediction of future states.
Data and intelligence are not just analytics. This is the awareness of the whole network.
In a world where speed is more important than size, the Gamble Hub makes intelligence the main tool for sustainable growth.
Key Topics
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Data enrichment
A practical guide to data enrichment for the iGaming ecosystem: sources and types of enriching signals (FX/geo/ASN/devices, KYC/RG/AML, content and directories), offline and streaming pipelines (lookup, join, UDF/ML features), normalization currency and timezone, PII privacy and minimization, quality and DQ rules, observability and lineage, cost and SLO, architecture patterns (dimension lookup, feature store, async enrichment), SQL/YAML/pseudocode examples, RACI and implementation roadmap.
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Streaming and streaming analytics
Practical methodology for building streaming and streaming analytics for iGaming: ingest→shina→obrabotka→serving architecture, windows and watermarks, CEP and stateful aggregation, exactly-once/idempotency, schemes and contracting, real-time showcases and ClickHouse/Pinot/Druid, observability and SLO, privacy and regionalization, cost-engineering, RACI and roadmap, with SQL/pseudocode examples.
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Real-time analytics
Full guide to real-time analytics for the iGaming ecosystem: business cases (AML/RG, operational SLAs, product personalization), ingest→shina→stream reference architecture - obrabotka→real-time showcases, CEP and stateful aggregations, watermarks/late data, online enrichment and Feature Store, metrics and SLO, observability and cost engineering, privacy and residency, SQL/pseudocode templates, RACI, and implementation roadmap.
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Reinforcement Training
RL Practice Guide (Renewal Learning) for iGaming: Cases (Personalization, Bonus Optimization, Game Recommendations, Operational Policies), Bandits/Contextual Bandits/Slate-RL, Offline/Batch-RL, Safe Limits (RG/AML/Compliance), Rewards, and Causal - evaluation, simulators and counterfactual-methods (IPS/DR), MLOps and serving (online/near-real-time), metrics and A/B, cost engineering, RACI, roadmap and checklists.
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Feature Engineering and Feature Selection
A practical guide to feature creation and selection for iGaming: point-in-time discipline, windows and aggregations (R/F/M), categorical encodings (TE/WOE), temporal/graph/NLP/geo-features, anti-leukage and online/offline reconciliation, Feature Store and tests equivalence, selection (filter/wrapper/embedded, SHAP/IV/MI), stability and drift, cost engineering (latency/cost per feature), RACI, roadmap, checklists and SQL/YAML/pseudocode examples.
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Model monitoring
ML model monitoring playbook in iGaming: SLI/SLO and operational metrics, data drift control/predictions (PSI/KL/KS), calibration (ECE), threshold stability and expected-cost, coverage and errors, slice/fairness analysis, online labels and delayed labels, alerts and runbook 'and, dashboards (Prometheus/Grafana/OTel), audit/PII/residency, RACI, roadmap and production readiness checklist.
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AI pipelines and training automation
Practical playbook on AI/ML pipeline design and automation in iGaming: orchestration (Airflow/Argo), data pipelines and feature (Feature Store), CT/CI/CD for models, registers and promotion policies, automatic retrain by drift, online/offline equivalence tests, security (PII/residency), RACI, roadmap, checklists and examples (DAG, YAML, pseudocode).
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KPIs and Benchmarks
System guide for KPIs and benchmarks: types of metrics (North Star, result/process, guardrail), formulas and norms, goal setting (SMART/OKR), normalization and seasonality, statistical stability, comparative bases (internal/external), dashboards, review cycles and anti-patterns (Goodhart).
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Key figure hierarchy
A practical guide to the hierarchy of indicators: how to choose North Star, decompose it into a driver tree, connect guardrail metrics, cascade goals by organization levels (OKR/KPI), agree on formulas in the semantic layer, set a freshness SLO and build a single cycle of review and development metrics.
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Correlation and Cause and Effect
A practical guide to correlation and causation: when correlation is sufficient, how to identify causality (A/B tests, DAG, back-door/front-door, IV, DiD, RDD, synthetic control), how to work with confounders, colliders and Simpson's paradox, and how to apply causal methods in the product marketing and ML.
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Conversion Analytics
A practical guide to conversion analytics: how to correctly read funnels and coefficients, set "correct denominators" and time windows, exclude bots and duplicates, build cohorts and segments, associate conversion with LTV/CAC/ROMI, conduct experiments and avoid typical traps. Templates for metrics passports, pseudo-SQL and checklists.
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Recommendation systems
Practical guide to building recommendation systems: data and attribute space, architecture (candidate recall → ranking → policy-aware re-rank), models (content-based, collaborative filtering, factorization/embeddings, LTR/neural networks, session, contextual bandits and RL), goals and limitations (value, diversification, fairness, RG/compliance), offline/online metrics, A/B and causal assessment, MLOps/observability, anti-patterns and checklists.
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Origin and data path
A practical guide to building Data Lineage in the section "Data and Intelligence": levels (business, technical, column), end-to-end linage from sources to ML models, events and contracts, glossary and metadata, graph visualization, impact analysis, SLO/SLI freshness and quality, scripts for iGaming (KYC/AML, game rounds, payments, Responsible Gaming), artifact templates, and an implementation roadmap.
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Data ethics and transparency
A practical guide to data ethics in the Data and Intelligence section: principles (benefit, non-harm, fairness, autonomy, responsibility), transparency for players and regulators, honest personalization and marketing without manipulation, consent and minimization of data, work with vulnerable groups, explainability of ML (model cards, data statements), fairness metrics, policy templates and checklists for implementation.
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Data tokenization
Data & Intelligence Tokenization How to Guide: What Tokens Are and How They Differ from Encryption, Options (vault-based, vaultless/FPE), Detokenization Schemes, Rotation and Key Lifecycle, Integration with KYC/AML, Payments and Logs, Access Policy and Auditing, Performance and Resiliency, Metrics and Roadmap implementation. With artifact patterns, RACI and anti-patterns.
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Data security and encryption
Complete data protection guide in Data & Intelligence: threat model, transit and storage encryption (TLS/mTLS, AES-GCM, ChaCha20-Poly1305, TDE, FLE/AEAD), key management (KMS/HSM, rotation, split-key, envelope), secret management, signature and integrity (HMM AC/ECDSA), tokenization and masking, DLP and log sanitization, backup and DR, access and audit (RBAC/ABAC, JIT), compliance and privacy, SLO metrics, checklists, RACI and implementation roadmap. Focusing on iGaming cases: KYC/AML, payments, gaming events, Responsible Gaming.
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Data auditing and versioning
Audit and versioning practice guide in Data & Intelligence: audit logs (who/what/when/why), integrity and signature controls, change policy (SEMVER for schemas and storefronts), time-travel and snapshots, SCD/CDF, contract evolution of schemas, versioned feature store and ML models, procedures rollback/backfill, RACI, SLO metrics, checklists, and roadmap. Examples for iGaming: GGR edits, retro provider feed corrections, KYC/AML and RG reporting.
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Computer vision in iGaming
Computer Vision Application Practice Guide in Data & Intelligence: KYC/OCR and liveness, anti-fraud (bots/multi-account), banner/video moderation, UI/QA control, stream analytics (eSports/streamers), responsible advertising (RG), brand protection, A/Creative, synthetic data generation, quality metrics, privacy/biometrics/DSAR, architectures (on-device/edge/cloud, TEE), MLOps, SLO and roadmap. With a focus on multi-brand and multi-jurisdictional platforms.
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Multimodal models
Complete Guide to Multimodal Models in Data & Intelligence: Scripts for iGaming (KYC/liveness, creative moderation, stream analysis, RG/anti-fraud, support), Architecture (CLIP-like, Encoder-Decoder, Perceiver, LLM-as-orchestrator), data and markup (synchronization of modalities, synthetics, PII-edition), alignment (contrastive, ITC/ITM, instruction-tuning), privacy/biometrics/DSAR, metrics and benchmarks, MLOps (registry, canary, drift), cost/latency (quantization, cache, routing), API and SLO templates, checklists and roadmap.
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Big data insights
A practical guide to extracting business insights from Big Data: architecture and pipelines, analysis methods (descriptive/diagnostic/predictive/prescriptive analytics), experiments and causality, data i治理 quality, privacy and security, MLOps and operational support, success metrics and monetization.
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Decision cycles
A complete guide to designing, measuring, and optimizing decision cycles from question-and-answer and data mining to experimentation, automation, and operational reporting. Frameworks (OODA/PDCA/DIKW), roles and rights, speed/quality metrics, data and tool architecture, anti-patterns, roadmap and checklists.
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Compress analytical data
A practical guide to data compression for analytics: column formats (Parquet/ORC), codecs (ZSTD/Snappy/LZ4), encodings (RLE/Dictionary/Delta/Frame-of-Reference/Gorilla/XOR), time series and log compression, sketch - structures (HLL/TDigest), lossy/lossless compromises, impact on cost and SLO, encryption and compliance, compression and storage policies, testing and antipatterns.
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Data integrity
A practical guide to ensuring data integrity throughout the circuit: integrity types (essential, reference, domain, business rules), contracts and schemes, transaction guarantees (ACID/isolation), distributed systems (idempotency, dedup, event order), DQ validation and tests, audit and lineage, security and privacy, roadmap and checklists.
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Data economics in iGaming
Practical guidance on economy of data in iGaming: the card of value and expenses (sborkhraneniyeobrabotkamodelideystviye), a unit economy (GGR, ARPPU, LTV, CAC, deduction), measurement of effect (uplift/increment), FinOps for data, prioritization of investments (real-time vs batch), compliance and privacy as a part of P&L, monetization of data (В2С/В2В/партнеры), the check sheets and templates the politician.
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AI visualization of metrics
AI visualization implementation guide: graph grammar and chart selection, NL→Viz (natural language in visual), auto-generation of dashboards, explanation of anomalies and causes, narratives and storytelling, RAG on metadata, quality and trust control, accessibility and privacy, SLO/cost, antipatterns, roadmap and checklists.